activity
20162023
most citedDeep Closest Point: Learning Representations for Point Cloud Registration

120 citations · 450 across the 14 of their papers we have counts for

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16 papers · 1 filter

cs.CV20233 cited

StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction

Tianyuan Yuan, Yicheng Liu, Yue Wang +2

High-Definition (HD) maps are essential for the safety of autonomous driving systems. While existing techniques employ camera images and onboard sensors to generate vectorized high…

cs.CV2023

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

Chenzhuang Du, Jiaye Teng, Tingle Li +5

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which…

cs.CV2023

'Tax-free' 3DMM Conditional Face Generation

Yiwen Huang, Zhiqiu Yu, Xinjie Yi +2

3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN an…

cs.CV20233 cited

GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training

Xiaoyu Tian, Haoxi Ran, Yue Wang +1

This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without ann…

cs.CV2023

Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

Xiaoyu Tian, Tao Jiang, Longfei Yun +5

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and…

cs.CV2023

Neural Map Prior for Autonomous Driving

Xuan Xiong, Yicheng Liu, Tianyuan Yuan +3

High-definition (HD) semantic maps are crucial in enabling autonomous vehicles to navigate urban environments. The traditional method of creating offline HD maps involves labor-int…